Overview

Dataset statistics

Number of variables17
Number of observations30135
Missing cells0
Missing cells (%)0.0%
Duplicate rows696
Duplicate rows (%)2.3%
Total size in memory3.9 MiB
Average record size in memory136.0 B

Variable types

Numeric15
Categorical2

Alerts

Dataset has 696 (2.3%) duplicate rowsDuplicates
Temperature is highly overall correlated with Feels LikeHigh correlation
Feels Like is highly overall correlated with TemperatureHigh correlation
Clouds is highly overall correlated with Weather MainHigh correlation
Rain 1h is highly overall correlated with Weather SeverityHigh correlation
Snow 1h is highly overall correlated with Weather MainHigh correlation
Departure Gate is highly overall correlated with Arrival IATA CodeHigh correlation
Arrival IATA Code is highly overall correlated with Departure GateHigh correlation
Weather Main is highly overall correlated with Clouds and 2 other fieldsHigh correlation
Weather Severity is highly overall correlated with Rain 1h and 1 other fieldsHigh correlation
Weather Main has 3154 (10.5%) zerosZeros
Weekday of Departure has 3990 (13.2%) zerosZeros

Reproduction

Analysis started2024-02-21 19:48:58.035281
Analysis finished2024-02-21 19:49:30.154690
Duration32.12 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Departure Delay (min)
Real number (ℝ)

Distinct240
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5090339 × 10-17
Minimum-0.86635383
Maximum44.336026
Zeros0
Zeros (%)0.0%
Negative18487
Negative (%)61.3%
Memory size235.6 KiB
2024-02-21T14:49:30.264921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.86635383
5-th percentile-0.83631903
Q1-0.44586658
median-0.17555335
Q30.21489909
95-th percentile1.0859084
Maximum44.336026
Range45.202379
Interquartile range (IQR)0.66076568

Descriptive statistics

Standard deviation1.0000166
Coefficient of variation (CV)6.6268665 × 1016
Kurtosis488.56126
Mean1.5090339 × 10-17
Median Absolute Deviation (MAD)0.30034804
Skewness14.842578
Sum3.4106051 × 10-13
Variance1.0000332
MonotonicityNot monotonic
2024-02-21T14:49:30.426098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8663538346 1471
 
4.9%
-0.2356229594 994
 
3.3%
-0.4158317809 917
 
3.0%
-0.3857969773 911
 
3.0%
-0.265657763 904
 
3.0%
-0.4458665845 850
 
2.8%
-0.475901388 835
 
2.8%
0.03469027289 818
 
2.7%
-0.115483745 813
 
2.7%
-0.3257273701 811
 
2.7%
Other values (230) 20811
69.1%
ValueCountFrequency (%)
-0.8663538346 1471
4.9%
-0.8363190311 95
 
0.3%
-0.8062842275 133
 
0.4%
-0.7762494239 182
 
0.6%
-0.7462146203 223
 
0.7%
-0.7161798167 274
 
0.9%
-0.6861450131 309
 
1.0%
-0.6561102096 363
 
1.2%
-0.626075406 402
 
1.3%
-0.5960406024 501
 
1.7%
ValueCountFrequency (%)
44.33602556 1
< 0.1%
43.22473783 1
< 0.1%
42.38376333 1
< 0.1%
35.4757585 1
< 0.1%
32.02175609 1
< 0.1%
24.36288118 1
< 0.1%
22.56079296 1
< 0.1%
20.15800867 1
< 0.1%
19.85766064 1
< 0.1%
14.30122198 1
< 0.1%

Temperature
Real number (ℝ)

HIGH CORRELATION 

Distinct1547
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.5451693 × 10-18
Minimum-2.4771146
Maximum3.2709554
Zeros0
Zeros (%)0.0%
Negative16857
Negative (%)55.9%
Memory size235.6 KiB
2024-02-21T14:49:30.575869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.4771146
5-th percentile-1.5230438
Q1-0.72266463
median-0.098439826
Q30.59553737
95-th percentile1.765486
Maximum3.2709554
Range5.7480701
Interquartile range (IQR)1.318202

Descriptive statistics

Standard deviation1.0000166
Coefficient of variation (CV)-1.3253733 × 1017
Kurtosis0.36232533
Mean-7.5451693 × 10-18
Median Absolute Deviation (MAD)0.66205661
Skewness0.52139409
Sum-6.3948846 × 10-13
Variance1.0000332
MonotonicityNot monotonic
2024-02-21T14:49:30.722100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.526590511 151
 
0.5%
-1.644814905 129
 
0.4%
-0.2615894899 114
 
0.4%
-1.053692933 104
 
0.3%
-0.3691736889 100
 
0.3%
-0.344346566 92
 
0.3%
-0.1339071439 91
 
0.3%
-0.2970568082 89
 
0.3%
-1.136450009 77
 
0.3%
-0.2273044155 77
 
0.3%
Other values (1537) 29111
96.6%
ValueCountFrequency (%)
-2.477114643 4
 
< 0.1%
-2.471203423 5
 
< 0.1%
-2.448740788 19
0.1%
-2.40145103 15
< 0.1%
-2.367165956 5
 
< 0.1%
-2.335245369 1
 
< 0.1%
-2.329334149 12
 
< 0.1%
-2.32342293 27
0.1%
-2.310418246 34
0.1%
-2.296231319 30
0.1%
ValueCountFrequency (%)
3.270955417 17
0.1%
3.235488099 11
 
< 0.1%
3.220118928 31
0.1%
3.1279039 32
0.1%
3.126721656 23
0.1%
3.051058044 17
0.1%
3.042782336 18
0.1%
3.040417848 24
0.1%
3.019137457 11
 
< 0.1%
3.017955213 28
0.1%

Feels Like
Real number (ℝ)

HIGH CORRELATION 

Distinct1654
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.6976631 × 10-17
Minimum-2.4489174
Maximum3.0999136
Zeros0
Zeros (%)0.0%
Negative16437
Negative (%)54.5%
Memory size235.6 KiB
2024-02-21T14:49:30.896765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.4489174
5-th percentile-1.5400485
Q1-0.70364754
median-0.069552944
Q30.59876264
95-th percentile1.7572434
Maximum3.0999136
Range5.548831
Interquartile range (IQR)1.3024102

Descriptive statistics

Standard deviation1.0000166
Coefficient of variation (CV)-5.890548 × 1016
Kurtosis0.12427359
Mean-1.6976631 × 10-17
Median Absolute Deviation (MAD)0.65321809
Skewness0.42604596
Sum-2.2737368 × 10-13
Variance1.0000332
MonotonicityNot monotonic
2024-02-21T14:49:31.064424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.004130485025 99
 
0.3%
-1.733296396 87
 
0.3%
-0.9431943938 86
 
0.3%
0.01801250103 76
 
0.3%
-0.05042945588 73
 
0.2%
0.2807088357 72
 
0.2%
-0.6100431036 71
 
0.2%
0.06129197379 70
 
0.2%
-0.3252037829 68
 
0.2%
-1.595405983 66
 
0.2%
Other values (1644) 29367
97.5%
ValueCountFrequency (%)
-2.448917446 5
 
< 0.1%
-2.3623585 27
0.1%
-2.31605953 14
 
< 0.1%
-2.309014034 22
0.1%
-2.301968538 4
 
< 0.1%
-2.294923043 12
 
< 0.1%
-2.278819053 48
0.2%
-2.271773557 5
 
< 0.1%
-2.259695565 11
 
< 0.1%
-2.230507083 15
 
< 0.1%
ValueCountFrequency (%)
3.099913561 17
0.1%
3.079783574 31
0.1%
3.052608091 11
 
< 0.1%
3.040530098 23
0.1%
2.995237627 32
0.1%
2.953971153 28
0.1%
2.950951655 18
0.1%
2.936860663 17
0.1%
2.927802169 11
 
< 0.1%
2.92578917 20
0.1%

Pressure
Real number (ℝ)

Distinct82
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.3674888 × 10-15
Minimum-3.447809
Maximum2.196717
Zeros0
Zeros (%)0.0%
Negative13755
Negative (%)45.6%
Memory size235.6 KiB
2024-02-21T14:49:31.243207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-3.447809
5-th percentile-1.6960595
Q1-0.625546
median0.1530093
Q30.63960637
95-th percentile1.5446769
Maximum2.196717
Range5.6445259
Interquartile range (IQR)1.2651524

Descriptive statistics

Standard deviation1.0000166
Coefficient of variation (CV)-2.2896832 × 1014
Kurtosis0.5410609
Mean-4.3674888 × 10-15
Median Absolute Deviation (MAD)0.58391648
Skewness-0.5482821
Sum-1.3164936 × 10-10
Variance1.0000332
MonotonicityNot monotonic
2024-02-21T14:49:31.401588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3476481303 1751
 
5.8%
0.2503287174 1531
 
5.1%
0.4449675432 1406
 
4.7%
0.7369257819 1192
 
4.0%
-0.33358776 1183
 
3.9%
0.1530093045 1174
 
3.9%
-0.4309071729 1108
 
3.7%
0.639606369 1090
 
3.6%
0.05568989161 1032
 
3.4%
0.5422869561 1029
 
3.4%
Other values (72) 17639
58.5%
ValueCountFrequency (%)
-3.447808973 111
0.4%
-3.35048956 90
0.3%
-3.253170147 26
 
0.1%
-3.155850734 48
0.2%
-3.058531321 52
0.2%
-2.961211908 31
 
0.1%
-2.863892495 3
 
< 0.1%
-2.766573082 45
0.1%
-2.66925367 87
0.3%
-2.571934257 51
0.2%
ValueCountFrequency (%)
2.196716975 33
 
0.1%
2.099397562 42
 
0.1%
2.00207815 78
 
0.3%
1.904758737 388
1.3%
1.807439324 525
1.7%
1.710119911 292
1.0%
1.612800498 149
 
0.5%
1.515481085 209
 
0.7%
1.418161672 331
1.1%
1.320842259 528
1.8%

Humidity
Real number (ℝ)

Distinct82
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.4187646 × 10-16
Minimum-3.3052405
Maximum1.8358394
Zeros0
Zeros (%)0.0%
Negative13957
Negative (%)46.3%
Memory size235.6 KiB
2024-02-21T14:49:31.554311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-3.3052405
5-th percentile-1.7367754
Q1-0.69113204
median0.093100486
Q30.79019607
95-th percentile1.4001547
Maximum1.8358394
Range5.1410799
Interquartile range (IQR)1.4813281

Descriptive statistics

Standard deviation1.0000166
Coefficient of variation (CV)-4.1344105 × 1015
Kurtosis-0.48301053
Mean-2.4187646 × 10-16
Median Absolute Deviation (MAD)0.78423253
Skewness-0.35990421
Sum-6.8212103 × 10-12
Variance1.0000332
MonotonicityNot monotonic
2024-02-21T14:49:31.712445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.400154698 1158
 
3.8%
0.1802374337 1142
 
3.8%
-0.08117340872 1021
 
3.4%
0.005963538758 1021
 
3.4%
0.5287852236 1000
 
3.3%
-0.2554473037 997
 
3.3%
0.09310048624 965
 
3.2%
0.3545113287 944
 
3.1%
1.313017751 927
 
3.1%
1.225880803 923
 
3.1%
Other values (72) 20037
66.5%
ValueCountFrequency (%)
-3.305240465 16
 
0.1%
-3.043829623 10
 
< 0.1%
-2.956692675 32
 
0.1%
-2.869555728 49
 
0.2%
-2.782418781 60
0.2%
-2.695281833 34
 
0.1%
-2.608144886 13
 
< 0.1%
-2.521007938 20
 
0.1%
-2.433870991 129
0.4%
-2.346734043 114
0.4%
ValueCountFrequency (%)
1.835839436 51
 
0.2%
1.748702488 157
 
0.5%
1.661565541 97
 
0.3%
1.574428593 380
 
1.3%
1.487291646 795
2.6%
1.400154698 1158
3.8%
1.313017751 927
3.1%
1.243308193 2
 
< 0.1%
1.225880803 923
3.1%
1.138743856 821
2.7%

Wind Speed
Real number (ℝ)

Distinct348
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2732473 × 10-16
Minimum-1.3146317
Maximum5.5019605
Zeros0
Zeros (%)0.0%
Negative17851
Negative (%)59.2%
Memory size235.6 KiB
2024-02-21T14:49:31.883067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1.3146317
5-th percentile-1.1158329
Q1-0.72265287
median-0.32505514
Q30.50548234
95-th percentile2.0958733
Maximum5.5019605
Range6.8165922
Interquartile range (IQR)1.2281352

Descriptive statistics

Standard deviation1.0000166
Coefficient of variation (CV)7.854064 × 1015
Kurtosis1.9624162
Mean1.2732473 × 10-16
Median Absolute Deviation (MAD)0.59197884
Skewness1.3733848
Sum5.1159077 × 10-12
Variance1.0000332
MonotonicityNot monotonic
2024-02-21T14:49:32.034222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9214517378 3152
 
10.5%
-0.7226528722 3036
 
10.1%
-1.115832851 1976
 
6.6%
-0.5238540065 1953
 
6.5%
-0.3250551409 1351
 
4.5%
0.7307877234 1208
 
4.0%
0.2757592087 960
 
3.2%
0.05045382758 945
 
3.1%
0.9560931044 801
 
2.7%
1.185816238 787
 
2.6%
Other values (338) 13966
46.3%
ValueCountFrequency (%)
-1.314631717 130
 
0.4%
-1.239529923 12
 
< 0.1%
-1.173263634 10
 
< 0.1%
-1.137921614 10
 
< 0.1%
-1.115832851 1976
6.6%
-1.102579593 46
 
0.2%
-1.076073078 23
 
0.1%
-1.049566562 26
 
0.1%
-1.04514881 9
 
< 0.1%
-1.0274778 17
 
0.1%
ValueCountFrequency (%)
5.501960499 1
 
< 0.1%
4.596321222 13
 
< 0.1%
4.366598089 38
 
0.1%
4.141292707 69
 
0.2%
3.911569574 13
 
< 0.1%
3.686264193 73
 
0.2%
3.456541059 61
 
0.2%
3.231235678 109
0.4%
3.001512544 118
0.4%
2.776207163 188
0.6%

Wind Degree
Real number (ℝ)

Distinct267
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2826788 × 10-16
Minimum-1.7405793
Maximum1.6747488
Zeros0
Zeros (%)0.0%
Negative12551
Negative (%)41.6%
Memory size235.6 KiB
2024-02-21T14:49:32.194789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1.7405793
5-th percentile-1.6457091
Q1-1.0954617
median0.39400078
Q30.82091679
95-th percentile1.3901381
Maximum1.6747488
Range3.4153281
Interquartile range (IQR)1.9163785

Descriptive statistics

Standard deviation1.0000166
Coefficient of variation (CV)-7.7963136 × 1015
Kurtosis-1.1953445
Mean-1.2826788 × 10-16
Median Absolute Deviation (MAD)0.61665646
Skewness-0.40811194
Sum-4.7748472 × 10-12
Variance1.0000332
MonotonicityNot monotonic
2024-02-21T14:49:32.385412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6122022996 2702
 
9.0%
0.8209167945 2563
 
8.5%
0.3940007822 1131
 
3.8%
1.039118312 1046
 
3.5%
0.1852862873 981
 
3.3%
-1.522377762 970
 
3.2%
-1.455968604 928
 
3.1%
-1.313663267 913
 
3.0%
-0.03291523006 896
 
3.0%
-1.740579279 850
 
2.8%
Other values (257) 17155
56.9%
ValueCountFrequency (%)
-1.740579279 850
2.8%
-1.731092257 32
 
0.1%
-1.721605234 12
 
< 0.1%
-1.712118212 36
 
0.1%
-1.702631189 24
 
0.1%
-1.693144167 19
 
0.1%
-1.664683099 1
 
< 0.1%
-1.645709054 579
1.9%
-1.607760964 11
 
< 0.1%
-1.598273942 90
 
0.3%
ValueCountFrequency (%)
1.674748819 316
1.0%
1.665261797 17
 
0.1%
1.655774774 7
 
< 0.1%
1.627313707 1
 
< 0.1%
1.617826684 16
 
0.1%
1.579878594 281
0.9%
1.551417527 20
 
0.1%
1.541930504 25
 
0.1%
1.485008369 132
0.4%
1.475521347 11
 
< 0.1%

Clouds
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8988695 × 10-17
Minimum-2.2208169
Maximum0.66580241
Zeros0
Zeros (%)0.0%
Negative9654
Negative (%)32.0%
Memory size235.6 KiB
2024-02-21T14:49:32.583520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.2208169
5-th percentile-2.0764859
Q1-0.45997912
median0.66580241
Q30.66580241
95-th percentile0.66580241
Maximum0.66580241
Range2.8866193
Interquartile range (IQR)1.1257815

Descriptive statistics

Standard deviation1.0000166
Coefficient of variation (CV)1.1237569 × 1016
Kurtosis-0.17129185
Mean8.8988695 × 10-17
Median Absolute Deviation (MAD)0
Skewness-1.2083876
Sum-1.8189894 × 10-12
Variance1.0000332
MonotonicityNot monotonic
2024-02-21T14:49:33.046953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6658024093 16452
54.6%
-0.05585241982 939
 
3.1%
-2.047619748 670
 
2.2%
-2.220816907 627
 
2.1%
0.6369362161 437
 
1.5%
-2.018753555 369
 
1.2%
-2.191950714 353
 
1.2%
0.5214714435 327
 
1.1%
0.4926052503 299
 
1.0%
0.5503376366 290
 
1.0%
Other values (113) 9372
31.1%
ValueCountFrequency (%)
-2.220816907 627
2.1%
-2.191950714 353
1.2%
-2.163084521 130
 
0.4%
-2.134218328 248
 
0.8%
-2.105352135 140
 
0.5%
-2.076485941 220
 
0.7%
-2.047619748 670
2.2%
-2.018753555 369
1.2%
-1.989887362 145
 
0.5%
-1.961021169 172
 
0.6%
ValueCountFrequency (%)
0.6658024093 16452
54.6%
0.6600291707 1
 
< 0.1%
0.6369362161 437
 
1.5%
0.608070023 245
 
0.8%
0.5907503071 1
 
< 0.1%
0.5792038298 242
 
0.8%
0.5561108753 1
 
< 0.1%
0.5503376366 290
 
1.0%
0.5214714435 327
 
1.1%
0.5041517276 2
 
< 0.1%

Rain 1h
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2912997 × 10-18
Minimum-0.19823706
Maximum14.208894
Zeros0
Zeros (%)0.0%
Negative27924
Negative (%)92.7%
Memory size235.6 KiB
2024-02-21T14:49:33.204162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.19823706
5-th percentile-0.19823706
Q1-0.19823706
median-0.19823706
Q3-0.19823706
95-th percentile0.78275587
Maximum14.208894
Range14.407131
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0000166
Coefficient of variation (CV)7.744264 × 1017
Kurtosis78.887993
Mean1.2912997 × 10-18
Median Absolute Deviation (MAD)0
Skewness7.8915796
Sum0
Variance1.0000332
MonotonicityNot monotonic
2024-02-21T14:49:33.357856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.198237058 27920
92.6%
0.7827558656 472
 
1.6%
1.475221459 73
 
0.2%
1.26363475 70
 
0.2%
4.360494763 53
 
0.2%
1.629102702 48
 
0.2%
1.340575371 48
 
0.2%
1.725278478 47
 
0.2%
2.360038605 46
 
0.2%
2.148451896 43
 
0.1%
Other values (82) 1315
 
4.4%
ValueCountFrequency (%)
-0.198237058 27920
92.6%
-0.002038473276 4
 
< 0.1%
0.01334965102 1
 
< 0.1%
0.05181996174 21
 
0.1%
0.1095254278 5
 
< 0.1%
0.1479957386 1
 
< 0.1%
0.1672308939 16
 
0.1%
0.1864660493 1
 
< 0.1%
0.22493636 32
 
0.1%
0.2441715154 24
 
0.1%
ValueCountFrequency (%)
14.20889431 29
0.1%
11.61214834 22
0.1%
11.26591554 15
< 0.1%
9.72710311 1
 
< 0.1%
9.150048449 35
0.1%
7.89976335 8
 
< 0.1%
7.842057884 23
0.1%
7.284238379 18
0.1%
6.82259465 5
 
< 0.1%
6.437891543 5
 
< 0.1%

Snow 1h
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0525811 × 10-20
Minimum-0.17873054
Maximum25.51671
Zeros0
Zeros (%)0.0%
Negative28347
Negative (%)94.1%
Memory size235.6 KiB
2024-02-21T14:49:33.514193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.17873054
5-th percentile-0.17873054
Q1-0.17873054
median-0.17873054
Q3-0.17873054
95-th percentile0.64391066
Maximum25.51671
Range25.69544
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0000166
Coefficient of variation (CV)2.4676041 × 1019
Kurtosis113.1547
Mean4.0525811 × 10-20
Median Absolute Deviation (MAD)0
Skewness9.0890642
Sum-9.094947 × 10-13
Variance1.0000332
MonotonicityNot monotonic
2024-02-21T14:49:33.672907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1787305426 28345
94.1%
0.4019573682 114
 
0.4%
1.418161212 95
 
0.3%
0.3051760497 79
 
0.3%
0.6923013235 70
 
0.2%
5.095851313 61
 
0.2%
1.708505167 57
 
0.2%
1.853677145 52
 
0.2%
0.4503480274 47
 
0.2%
0.9342546197 41
 
0.1%
Other values (79) 1174
 
3.9%
ValueCountFrequency (%)
-0.1787305426 28345
94.1%
-0.05291482858 1
 
< 0.1%
-0.03355856488 1
 
< 0.1%
0.02451022619 1
 
< 0.1%
0.03418835803 2
 
< 0.1%
0.1406478083 1
 
< 0.1%
0.3051760497 79
 
0.3%
0.4019573682 114
 
0.4%
0.4503480274 47
 
0.2%
0.4987386866 9
 
< 0.1%
ValueCountFrequency (%)
25.51670951 1
 
< 0.1%
17.48386008 1
 
< 0.1%
16.0321403 29
0.1%
15.11271777 6
 
< 0.1%
10.66077712 17
0.1%
7.950900208 1
 
< 0.1%
7.708946911 1
 
< 0.1%
7.466993615 22
0.1%
7.273430978 13
< 0.1%
7.17664966 21
0.1%

Departure Gate
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.351385
Minimum0
Maximum74
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size235.6 KiB
2024-02-21T14:49:33.836962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q131
median49
Q359
95-th percentile70
Maximum74
Range74
Interquartile range (IQR)28

Descriptive statistics

Standard deviation17.23673
Coefficient of variation (CV)0.38007064
Kurtosis-0.82965202
Mean45.351385
Median Absolute Deviation (MAD)13
Skewness-0.33396703
Sum1366664
Variance297.10487
MonotonicityNot monotonic
2024-02-21T14:49:34.002597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 4844
 
16.1%
66 751
 
2.5%
41 674
 
2.2%
65 671
 
2.2%
29 654
 
2.2%
28 642
 
2.1%
30 632
 
2.1%
18 630
 
2.1%
69 598
 
2.0%
64 591
 
2.0%
Other values (65) 19448
64.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 3
 
< 0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
4 116
0.4%
5 4
 
< 0.1%
6 53
0.2%
7 3
 
< 0.1%
8 18
 
0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
74 161
 
0.5%
73 344
1.1%
72 396
1.3%
71 581
1.9%
70 524
1.7%
69 598
2.0%
68 541
1.8%
67 578
1.9%
66 751
2.5%
65 671
2.2%

Arrival IATA Code
Real number (ℝ)

HIGH CORRELATION 

Distinct267
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.92398
Minimum0
Maximum266
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size235.6 KiB
2024-02-21T14:49:34.163293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q176
median155
Q3242
95-th percentile261
Maximum266
Range266
Interquartile range (IQR)166

Descriptive statistics

Standard deviation82.040251
Coefficient of variation (CV)0.52280253
Kurtosis-1.388409
Mean156.92398
Median Absolute Deviation (MAD)84
Skewness-0.16080862
Sum4728904
Variance6730.6027
MonotonicityNot monotonic
2024-02-21T14:49:34.323634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
261 2662
 
8.8%
114 1947
 
6.5%
246 1620
 
5.4%
71 1106
 
3.7%
237 1057
 
3.5%
155 899
 
3.0%
240 883
 
2.9%
229 799
 
2.7%
50 700
 
2.3%
76 628
 
2.1%
Other values (257) 17834
59.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 47
0.2%
2 3
 
< 0.1%
3 84
0.3%
4 35
0.1%
5 86
0.3%
6 6
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 39
0.1%
ValueCountFrequency (%)
266 16
 
0.1%
265 112
 
0.4%
264 13
 
< 0.1%
263 68
 
0.2%
262 117
 
0.4%
261 2662
8.8%
260 255
 
0.8%
259 7
 
< 0.1%
258 10
 
< 0.1%
257 178
 
0.6%

Airline Name
Real number (ℝ)

Distinct145
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.617222
Minimum0
Maximum144
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size235.6 KiB
2024-02-21T14:49:34.478605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median34
Q3100
95-th percentile134
Maximum144
Range144
Interquartile range (IQR)95

Descriptive statistics

Standard deviation50.51486
Coefficient of variation (CV)0.92488885
Kurtosis-1.4113985
Mean54.617222
Median Absolute Deviation (MAD)29
Skewness0.46747445
Sum1645890
Variance2551.7511
MonotonicityNot monotonic
2024-02-21T14:49:34.631818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 6846
22.7%
134 3342
 
11.1%
18 2451
 
8.1%
81 1740
 
5.8%
6 1372
 
4.6%
121 1023
 
3.4%
48 827
 
2.7%
96 795
 
2.6%
123 697
 
2.3%
10 664
 
2.2%
Other values (135) 10378
34.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 372
 
1.2%
2 219
 
0.7%
3 283
 
0.9%
4 82
 
0.3%
5 6846
22.7%
6 1372
 
4.6%
7 1
 
< 0.1%
8 30
 
0.1%
9 366
 
1.2%
ValueCountFrequency (%)
144 1
 
< 0.1%
143 2
 
< 0.1%
142 573
1.9%
141 1
 
< 0.1%
140 1
 
< 0.1%
139 20
 
0.1%
138 1
 
< 0.1%
137 1
 
< 0.1%
136 10
 
< 0.1%
135 2
 
< 0.1%

Weather Main
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5800564
Minimum0
Maximum6
Zeros3154
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size235.6 KiB
2024-02-21T14:49:34.764669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6161129
Coefficient of variation (CV)1.0228198
Kurtosis1.7527549
Mean1.5800564
Median Absolute Deviation (MAD)0
Skewness1.7729882
Sum47615
Variance2.611821
MonotonicityNot monotonic
2024-02-21T14:49:34.857039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 21613
71.7%
0 3154
 
10.5%
5 1931
 
6.4%
6 1689
 
5.6%
4 1335
 
4.4%
2 366
 
1.2%
3 47
 
0.2%
ValueCountFrequency (%)
0 3154
 
10.5%
1 21613
71.7%
2 366
 
1.2%
3 47
 
0.2%
4 1335
 
4.4%
5 1931
 
6.4%
6 1689
 
5.6%
ValueCountFrequency (%)
6 1689
 
5.6%
5 1931
 
6.4%
4 1335
 
4.4%
3 47
 
0.2%
2 366
 
1.2%
1 21613
71.7%
0 3154
 
10.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2.0
11454 
0.0
9883 
1.0
8656 
3.0
 
142

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90405
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 11454
38.0%
0.0 9883
32.8%
1.0 8656
28.7%
3.0 142
 
0.5%

Length

2024-02-21T14:49:34.968364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T14:49:35.107357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 11454
38.0%
0.0 9883
32.8%
1.0 8656
28.7%
3.0 142
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 40018
44.3%
. 30135
33.3%
2 11454
 
12.7%
1 8656
 
9.6%
3 142
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60270
66.7%
Other Punctuation 30135
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40018
66.4%
2 11454
 
19.0%
1 8656
 
14.4%
3 142
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 30135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90405
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40018
44.3%
. 30135
33.3%
2 11454
 
12.7%
1 8656
 
9.6%
3 142
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40018
44.3%
. 30135
33.3%
2 11454
 
12.7%
1 8656
 
9.6%
3 142
 
0.2%

Weekday of Departure
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0495769
Minimum0
Maximum6
Zeros3990
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size235.6 KiB
2024-02-21T14:49:35.206862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0109042
Coefficient of variation (CV)0.65940433
Kurtosis-1.2825809
Mean3.0495769
Median Absolute Deviation (MAD)2
Skewness-0.022508721
Sum91899
Variance4.0437357
MonotonicityNot monotonic
2024-02-21T14:49:35.301224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 4824
16.0%
5 4605
15.3%
6 4508
15.0%
3 4478
14.9%
4 4054
13.5%
0 3990
13.2%
2 3676
12.2%
ValueCountFrequency (%)
0 3990
13.2%
1 4824
16.0%
2 3676
12.2%
3 4478
14.9%
4 4054
13.5%
5 4605
15.3%
6 4508
15.0%
ValueCountFrequency (%)
6 4508
15.0%
5 4605
15.3%
4 4054
13.5%
3 4478
14.9%
2 3676
12.2%
1 4824
16.0%
0 3990
13.2%

Weather Severity
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1.0
26151 
0.0
3984 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90405
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 26151
86.8%
0.0 3984
 
13.2%

Length

2024-02-21T14:49:35.415541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T14:49:35.529858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 26151
86.8%
0.0 3984
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 34119
37.7%
. 30135
33.3%
1 26151
28.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60270
66.7%
Other Punctuation 30135
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34119
56.6%
1 26151
43.4%
Other Punctuation
ValueCountFrequency (%)
. 30135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90405
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34119
37.7%
. 30135
33.3%
1 26151
28.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34119
37.7%
. 30135
33.3%
1 26151
28.9%

Interactions

2024-02-21T14:49:27.576699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:00.681854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:02.480543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:04.450696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:06.238824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:08.024836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:10.367788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:12.174515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:14.005183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:16.122290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:18.056995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:19.869596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:21.765368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:23.808342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:25.683803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:27.699962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:00.835541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:02.597819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:04.563052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:06.348153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:08.163018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:10.485077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:12.286530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:14.124542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:16.251494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:18.171284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:19.988926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:21.877649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:23.924727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:25.794217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:27.825161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:00.952686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:02.718153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:04.678401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:06.461514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:08.288495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:10.598886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:12.406857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:14.240170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:16.385652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:18.300798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:20.112586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:21.993972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:24.045602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:25.907917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-02-21T14:49:08.418452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:10.718918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:12.530137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:14.353426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:16.507946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:18.417095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:20.234662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:22.109716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:24.168204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:26.024294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:28.081139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:01.186827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:02.962820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:04.907966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:06.687177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:08.557228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:10.833522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:12.651166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:14.468877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:16.627753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:18.531004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:20.357957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:22.227277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:24.290283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:26.140231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:28.205375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:01.299053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:03.099146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:05.023286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:06.798663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:08.687579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:10.949140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:12.766973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:14.584004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:16.746979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-02-21T14:49:22.343817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:24.409599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:26.260527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:28.336195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:01.416359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:03.233407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:05.142524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:06.917505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:08.843481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:11.071319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:12.888781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:14.705154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:16.875316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:18.766592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:20.604767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:22.466327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:24.534569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:26.384920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:28.487610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:01.535641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:03.373508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:05.268797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:07.055112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:09.011048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:11.202851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:13.012526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:14.826186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:17.008692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:18.890915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:20.728820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:22.593292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:24.663859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:26.519560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:28.618083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:01.648185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:03.493078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:05.410292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:07.176153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:09.174133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:11.324688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:13.133762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:14.941519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:17.134466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:19.017136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:20.853035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:22.711643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:24.788693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:26.643599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:28.748932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:01.765514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:03.613611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:05.530617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:07.299736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:09.321844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:11.447108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:13.260058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:15.065280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:17.263187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:19.150863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:20.981264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:22.836157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:24.918455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:26.778855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:28.873377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:01.881824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:03.728148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:05.642911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:07.413676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:09.625626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:11.563056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:13.378329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:15.202422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:17.387859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:19.264163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:21.105922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:22.950468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:25.044569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:26.901118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:29.011736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:02.005392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:03.848521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:05.767199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:07.545791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:09.791806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:11.694959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:13.509576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:15.357723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:17.522545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:19.391351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:21.232931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:23.077765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:25.175088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:27.039191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:29.137673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:02.120714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:03.962790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:05.879553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:07.662137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:09.939356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:11.809583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:13.629392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:15.504441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:17.652833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:19.504699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:21.378119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:23.192028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:25.298437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:27.170363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:29.271915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:02.242700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:04.087045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:06.002952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:07.785426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:10.099331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:11.936051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:13.757531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:15.657892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:17.798948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:19.628579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:21.512719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:23.318337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:25.430221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:27.307619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:29.400174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:02.361141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:04.205084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:06.120255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:07.903738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:10.234664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:12.055305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:13.875779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:15.991559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:17.933665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:19.748099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:21.640406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:23.437241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:25.555506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T14:49:27.435648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-02-21T14:49:35.638135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Departure Delay (min)TemperatureFeels LikePressureHumidityWind SpeedWind DegreeCloudsRain 1hSnow 1hDeparture GateArrival IATA CodeAirline NameWeather MainWeekday of DepartureDeparture Time of DayWeather Severity
Departure Delay (min)1.000-0.106-0.113-0.0350.0070.0690.0150.026-0.0030.093-0.030-0.0340.0110.049-0.0190.0080.016
Temperature-0.1061.0000.973-0.331-0.040-0.1200.0570.0940.215-0.1400.004-0.0130.0120.099-0.0720.1580.162
Feels Like-0.1130.9731.000-0.244-0.078-0.3100.0420.0350.190-0.1790.005-0.0130.0120.029-0.0790.1500.107
Pressure-0.035-0.331-0.2441.000-0.356-0.338-0.095-0.383-0.351-0.1680.012-0.001-0.005-0.4900.0700.0860.448
Humidity0.007-0.040-0.078-0.3561.0000.184-0.1750.3250.3300.2240.0070.029-0.0400.4810.0440.2220.466
Wind Speed0.069-0.120-0.310-0.3380.1841.0000.0540.2430.1350.195-0.0020.0020.0020.3010.0230.0710.265
Wind Degree0.0150.0570.042-0.095-0.1750.0541.000-0.184-0.026-0.174-0.009-0.0070.008-0.204-0.0230.0770.172
Clouds0.0260.0940.035-0.3830.3250.243-0.1841.0000.2230.203-0.0100.0010.0110.5830.0110.0840.277
Rain 1h-0.0030.2150.190-0.3510.3300.135-0.0260.2231.000-0.0710.000-0.001-0.0040.497-0.0790.0740.547
Snow 1h0.093-0.140-0.179-0.1680.2240.195-0.1740.203-0.0711.000-0.0050.0010.0010.5030.0030.0790.400
Departure Gate-0.0300.0040.0050.0120.007-0.002-0.009-0.0100.000-0.0051.000-0.5300.155-0.005-0.0030.1510.000
Arrival IATA Code-0.034-0.013-0.013-0.0010.0290.002-0.0070.001-0.0010.001-0.5301.000-0.095-0.0000.0090.1370.000
Airline Name0.0110.0120.012-0.005-0.0400.0020.0080.011-0.0040.0010.155-0.0951.0000.012-0.0150.2090.013
Weather Main0.0490.0990.029-0.4900.4810.301-0.2040.5830.4970.503-0.005-0.0000.0121.000-0.0730.0600.962
Weekday of Departure-0.019-0.072-0.0790.0700.0440.023-0.0230.011-0.0790.003-0.0030.009-0.015-0.0731.0000.0190.205
Departure Time of Day0.0080.1580.1500.0860.2220.0710.0770.0840.0740.0790.1510.1370.2090.0600.0191.0000.011
Weather Severity0.0160.1620.1070.4480.4660.2650.1720.2770.5470.4000.0000.0000.0130.9620.2050.0111.000

Missing values

2024-02-21T14:49:29.597859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-21T14:49:29.947921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Departure Delay (min)TemperatureFeels LikePressureHumidityWind SpeedWind DegreeCloudsRain 1hSnow 1hDeparture GateArrival IATA CodeAirline NameWeather MainDeparture Time of DayWeekday of DepartureWeather Severity
0-0.175553-0.592618-0.4560490.639606-1.823912-0.665222-1.503404-1.816690-0.198237-0.17873143.0117.01.01.01.00.01.0
10.124795-0.592618-0.4560490.639606-1.823912-0.665222-1.503404-1.816690-0.198237-0.17873145.038.044.01.01.00.01.0
2-0.866354-0.592618-0.4560490.639606-1.823912-0.665222-1.503404-1.816690-0.198237-0.17873138.05.010.01.01.00.01.0
32.587649-0.592618-0.4560490.639606-1.823912-0.665222-1.503404-1.816690-0.198237-0.17873130.0261.033.01.01.00.01.0
40.034690-0.592618-0.4560490.639606-1.823912-0.665222-1.503404-1.816690-0.198237-0.17873141.0261.095.01.01.00.01.0
5-0.235623-0.592618-0.4560490.639606-1.823912-0.665222-1.503404-1.816690-0.198237-0.17873171.0109.06.01.01.00.01.0
60.485212-0.592618-0.4560490.639606-1.823912-0.665222-1.503404-1.816690-0.198237-0.17873129.0256.0123.01.01.00.01.0
7-0.836319-0.592618-0.4560490.639606-1.823912-0.665222-1.503404-1.816690-0.198237-0.17873161.057.0134.01.01.00.01.0
8-0.505936-0.631632-0.6100430.542287-1.823912-0.333891-1.067001-1.325965-0.198237-0.17873127.0253.05.01.01.00.01.0
9-0.295693-0.631632-0.6100430.542287-1.823912-0.333891-1.067001-1.325965-0.198237-0.17873158.0114.0134.01.01.00.01.0
Departure Delay (min)TemperatureFeels LikePressureHumidityWind SpeedWind DegreeCloudsRain 1hSnow 1hDeparture GateArrival IATA CodeAirline NameWeather MainDeparture Time of DayWeekday of DepartureWeather Severity
301250.395108-1.577427-1.776576-0.2362680.0931000.9560930.7260470.665802-0.198237-0.17873148.0191.018.01.01.02.01.0
30126-0.475901-1.640086-1.540049-0.138949-1.736775-0.5238540.3940010.232810-0.198237-0.17873152.071.05.01.01.00.01.0
30127-0.866354-1.864712-2.021155-0.1389491.2258810.6866100.6786110.608070-0.198237-0.17873152.015.0118.01.00.02.01.0
301280.515247-1.666095-1.464561-0.138949-0.255447-0.7226530.8209170.636936-0.198237-0.17873152.0237.05.01.00.02.01.0
30129-0.235623-1.577427-1.776576-0.2362680.0931000.9560930.7260470.665802-0.198237-0.17873152.0114.05.01.01.02.01.0
301300.845630-2.118895-2.128851-0.2362681.138744-0.2499530.498358-0.488845-0.198237-0.17873147.0127.018.01.01.00.01.0
301311.836778-1.908455-2.058396-0.236268-1.1268170.9560930.8209170.665802-0.198237-0.17873152.0261.05.01.00.02.01.0
30132-0.866354-1.748852-1.922518-0.236268-0.3425841.1858160.9157870.665802-0.198237-0.17873152.0245.099.01.00.02.01.0
301330.365073-1.748852-1.922518-0.236268-0.3425841.1858160.9157870.665802-0.198237-0.17873152.0242.05.01.00.02.01.0
301340.244934-1.577427-1.776576-0.2362680.0931000.9560930.7260470.665802-0.198237-0.17873152.0114.05.01.01.02.01.0

Duplicate rows

Most frequently occurring

Departure Delay (min)TemperatureFeels LikePressureHumidityWind SpeedWind DegreeCloudsRain 1hSnow 1hDeparture GateArrival IATA CodeAirline NameWeather MainDeparture Time of DayWeekday of DepartureWeather Severity# duplicates
0-0.866354-2.170913-2.2788190.9315651.2258810.028365-1.5034040.261676-0.198237-0.17873164.065.0134.01.00.05.01.02
1-0.866354-1.781955-1.9507000.6396061.0516070.315519-1.465456-1.499162-0.198237-0.17873162.0128.018.01.02.05.01.02
2-0.866354-1.707474-1.5319970.4449681.574429-0.674058-1.4939170.636936-0.198237-0.17873115.0229.06.01.02.06.01.02
3-0.866354-1.705109-1.1807280.3476481.574429-1.137922-1.1808450.665802-0.198237-0.17873140.0169.010.01.02.06.01.02
4-0.866354-1.649544-1.4736200.4449681.574429-0.678475-1.4180210.665802-0.198237-0.17873152.0221.09.01.02.06.01.02
5-0.866354-1.305511-1.4434250.931565-0.5168580.227164-1.3705850.550338-0.198237-0.17873126.0250.092.01.00.05.01.02
6-0.866354-1.305511-1.4434250.931565-0.5168580.227164-1.3705850.550338-0.198237-0.17873140.0191.05.01.00.05.01.02
7-0.866354-1.239305-1.3226450.834245-0.6039950.019530-1.3326370.463739-0.198237-0.17873134.0171.06.01.00.05.01.02
8-0.866354-1.239305-1.3226450.834245-0.6039950.019530-1.3326370.463739-0.198237-0.17873167.0139.0134.01.00.05.01.02
9-0.866354-1.171917-1.4313470.736926-1.0396801.185816-1.4559690.665802-0.198237-0.17873152.0246.095.01.02.05.01.02